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Elements of Large-Scale Mathematical Programming Part I: Concepts

Author

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  • Arthur M. Geoffrion

    (University of California, Los Angeles)

Abstract

A framework of concepts is developed which helps to unify a substantial portion of the literature on large-scale mathematical programming. These concepts fall into two categories. The first category consists of problem manipulations that can be used to derive what are often referred to as "master" problems; the principal manipulations discussed are Projection, Inner Linearization, and Outer Linearization. The second category consists of solution strategies that can be used to solve the master problems, often with the result that "subproblems" arise which can then be solved by specialized algorithms. The Piecewise, Restriction, and Relaxation strategies are the principal ones discussed. Numerous algorithms found in the literature are classified according to the manipulation/strategy pattern they can be viewed as using, and the usefulness of the framework is demonstrated by using it (see Part II of this paper) to rederive a representative selection of algorithms. The material presented is listed in the following order: The first section is introductory in nature, and discusses types of large-scale problems, the scope of discussion and the literature, and the notation used. The second section, entitled "Problem Manipulations: Source of `Master' Problems" covers the subjects of projection, inner linearization and outer linearization. The third section, "Solution Strategies: Source of `Subproblems'," discusses piecewise strategy, restriction and relaxation. The fourth section is entitled "Synthesizing Known Algorithms from Manipulations and Strategies," and is followed by a concluding section and an extensive bibliography.

Suggested Citation

  • Arthur M. Geoffrion, 1970. "Elements of Large-Scale Mathematical Programming Part I: Concepts," Management Science, INFORMS, vol. 16(11), pages 652-675, July.
  • Handle: RePEc:inm:ormnsc:v:16:y:1970:i:11:p:652-675
    DOI: 10.1287/mnsc.16.11.652
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    Cited by:

    1. Xiang Li & Asgeir Tomasgard & Paul Barton, 2012. "Decomposition strategy for the stochastic pooling problem," Journal of Global Optimization, Springer, vol. 54(4), pages 765-790, December.
    2. Zhang, Jing & Zhuang, Jun & Behlendorf, Brandon, 2018. "Stochastic shortest path network interdiction with a case study of Arizona–Mexico border," Reliability Engineering and System Safety, Elsevier, vol. 179(C), pages 62-73.
    3. Akter, M.N. & Mahmud, M.A. & Haque, M.E. & Oo, Amanullah M.T., 2020. "An optimal distributed energy management scheme for solving transactive energy sharing problems in residential microgrids," Applied Energy, Elsevier, vol. 270(C).
    4. Rahmaniani, Ragheb & Crainic, Teodor Gabriel & Gendreau, Michel & Rei, Walter, 2017. "The Benders decomposition algorithm: A literature review," European Journal of Operational Research, Elsevier, vol. 259(3), pages 801-817.
    5. Syam, Siddhartha S., 1998. "A dual ascent method for the portfolio selection problem with multiple constraints and linked proposals," European Journal of Operational Research, Elsevier, vol. 108(1), pages 196-207, July.
    6. Ashtiani, Alireza M. & Ferreira, Paulo A.V., 2015. "A branch-and-cut algorithm for a class of sum-of-ratios problems," Applied Mathematics and Computation, Elsevier, vol. 268(C), pages 596-608.
    7. Roy E. Marsten, 1975. "The Use of the Box Step Method in Discrete Optimization," NBER Working Papers 0086, National Bureau of Economic Research, Inc.
    8. Queiroz, Marcelo & Humes, Carlos, 2003. "A heuristic for the continuous capacity and flow assignment," European Journal of Operational Research, Elsevier, vol. 146(3), pages 444-459, May.
    9. Ruth Misener & Christodoulos Floudas, 2014. "ANTIGONE: Algorithms for coNTinuous / Integer Global Optimization of Nonlinear Equations," Journal of Global Optimization, Springer, vol. 59(2), pages 503-526, July.
    10. Patriksson, Michael, 2008. "A survey on the continuous nonlinear resource allocation problem," European Journal of Operational Research, Elsevier, vol. 185(1), pages 1-46, February.
    11. Kaynov, Illya & van Knippenberg, Marijn & Menkovski, Vlado & van Breemen, Albert & van Jaarsveld, Willem, 2024. "Deep Reinforcement Learning for One-Warehouse Multi-Retailer inventory management," International Journal of Production Economics, Elsevier, vol. 267(C).
    12. Robbins, Thomas R. & Harrison, Terry P., 2010. "A stochastic programming model for scheduling call centers with global Service Level Agreements," European Journal of Operational Research, Elsevier, vol. 207(3), pages 1608-1619, December.
    13. Xiang Li & Asgeir Tomasgard & Paul I. Barton, 2011. "Nonconvex Generalized Benders Decomposition for Stochastic Separable Mixed-Integer Nonlinear Programs," Journal of Optimization Theory and Applications, Springer, vol. 151(3), pages 425-454, December.
    14. Meijboom, B.R., 1986. "Planning in decentralized firms : a contribution to the theory on multilevel decisions," Other publications TiSEM 291e5a70-7bb8-4de3-be1f-0, Tilburg University, School of Economics and Management.
    15. Boukouvala, Fani & Misener, Ruth & Floudas, Christodoulos A., 2016. "Global optimization advances in Mixed-Integer Nonlinear Programming, MINLP, and Constrained Derivative-Free Optimization, CDFO," European Journal of Operational Research, Elsevier, vol. 252(3), pages 701-727.
    16. Lahrichi, Nadia & Crainic, Teodor Gabriel & Gendreau, Michel & Rei, Walter & Crişan, Gloria Cerasela & Vidal, Thibaut, 2015. "An integrative cooperative search framework for multi-decision-attribute combinatorial optimization: Application to the MDPVRP," European Journal of Operational Research, Elsevier, vol. 246(2), pages 400-412.
    17. Throsby, C.D., 1973. "New Methodologies in Agricultural Production Economics: a Review," 1973 Conference, August 19-30, 1973, São Paulo, Brazil 181385, International Association of Agricultural Economists.
    18. Eduardo Muñoz & Mathias Stolpe, 2011. "Generalized Benders’ Decomposition for topology optimization problems," Journal of Global Optimization, Springer, vol. 51(1), pages 149-183, September.
    19. Tarim, S. Armagan & Dogru, Mustafa K. & Özen, Ulas & Rossi, Roberto, 2011. "An efficient computational method for a stochastic dynamic lot-sizing problem under service-level constraints," European Journal of Operational Research, Elsevier, vol. 215(3), pages 563-571, December.
    20. Walter Rei & Jean-François Cordeau & Michel Gendreau & Patrick Soriano, 2009. "Accelerating Benders Decomposition by Local Branching," INFORMS Journal on Computing, INFORMS, vol. 21(2), pages 333-345, May.
    21. Subramanian, Avinash S.R. & Kannan, Rohit & Holtorf, Flemming & Adams, Thomas A. & Gundersen, Truls & Barton, Paul I., 2023. "Optimization under uncertainty of a hybrid waste tire and natural gas feedstock flexible polygeneration system using a decomposition algorithm," Energy, Elsevier, vol. 284(C).

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